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Health effects studies Epidemiological studies Extensive application to air pollution Because of large degree of variation of air pollution levels over time and across geographic areas Inexpensive database Monitoring networks for regulatory objectives Routinely collected mortality and morbidity statistics by government and insurance agency

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Problems of epidemiological studies Time-domain methods to demonstrate associations between air pollution and various health effects in single cities. Two common features 1. Mainly carried out in places with a large population. 2. Aggregate data in a large area to represent population exposures. Misclassification is often compounded.

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Possible solutions Create less heterogeneous exposures by clustering hospitals around a monitoring station as suggested by Burnett et al. Exposure attribution based on clustered hospitals remains a serious challenge because some hospitals are located as far as 200 km away from any monitoring stations.

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Possible solutions Known census clusters will provide exposure populations with smaller and more homogeneous regions (Zidek et al.). Many important explanatory factors are either unmeasured or unavailable in all clusters. Census areas are not equivalent to clinic catchment areas. Daily outcomes in small census subdivision are sparse when the health outcome is the case for serious illness.

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Possible solutions Cluster clinics around a monitoring station to create relatively homogeneous area of size about 20 km 2. (Hwang and Chan, AJE 2002) Population exposure is represented by measurements from the monitoring station. Health outcome is daily clinic visit for minor lower respiratory illness. Two-phase modeling: time and space

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Design for this study Study areas: 20 small areas of townships/city districts where air quality monitoring stations situated Study population: sampled people in the National Health Insurance Research Database (NHIRD) who had visited clinics in the selected areas. Study period: 1997/01~2001/12

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The data Environmental variables from EPA Daily average for NO 2, SO 2 and PM 10 Daily maximum O 3 and maximum 8-hour running average for CO Daily average temperature and average dew point

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Statistical analysis Phase I: Use generalized linear mixed-effects models to model daily series of each month in the 20 areas to obtain estimated pollution coefficients on clinic visits for each month. Phase IIa: Average the estimated pollution coefficients across the time course. Phase IIb: Use Bayesian approach to combine the estimated pollution coefficients across the time course.

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Phase I Let Y itms be the clinic visit count at t-th day in the m-th month of the s-th year for the i-th area. For each month, fit the model

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Phase IIa Estimates of pollution coefficients and their standard errors are denoted by The average

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Health impact Measured as the percentage increase in clinic visits that corresponds to a 10% increase in air pollution levels. It is expressed by, where is the corresponding overall pollution level in the 5 years.

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Phase I results Average per cent increased risks of clinic visits for 10% increased of average pollution levels in the 20 areas in each month